Geophysical Imaging and Monitoring
Nino Menzel
M.Sc.
Nino Menzel
PhD researcherM.Sc. Nino Menzel
Geophysical Imaging and Monitoring
RWTH Aachen University
Wüllnerstr. 2 (Bergbaugebäude)
Room: 505d
52062 Aachen
Research interests
My research focuses on the area of geophysical monitoring and includes the optimization of experimental design as part of the survey planning, but also the practical application of geophysical methods in the field. Current and future activities are:
- Implementation of synthetic reference models for the possible host rocks of a nuclear waste repository in Germany (rock salt, claystone, crystalline rock)
- Optimized Experimental Design (OED) techniques for monitoring of radionuclide transport
- Adaption of OED to applications in geophysical and petrophysical joint inversion
- Application of Optimized Experimental Design in the field – comparison of conventional and optimized surveys
Professional experience
| since Oct. 2022 | Doctoral researcher at the department of Geophysical Imaging and Monitoring, RWTH Aachen University. |
| May 2020 – Aug. 2022 | Undergraduate assistant at Altenbockum und Partner, Geologen |
Education
| 2019 – 2022 | Applied geosciences (M.Sc.) at RWTH Aachen University focussing on geophysics, hydrogeology and engineering geology. |
| 2015 – 2018 | Geowissenschaften (B.Sc.) at the University of Cologne focussing on geophysics, sedimentology and palaeontology. |
Awards
| Nov. 2024 | Outstanding Student and PhD candidate Presentation (OSPP) Award by the European Geosciences Union |
| Mar. 2023 | Poster award at the annual meeting of the German Geophysical Society |
Publications
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Prospection of faults on the Southern Erftscholle (Germany) with individually and jointly inverted refraction seismics and electrical resistivity tomography
2024 |
Journal of Applied Geophysics, doi:10.1016/j.jappgeo.2024.105549
PDFNote: This publication resulted from Nino Menzel's master thesis.Abstract
As part of the Lower Rhein Embayment (LRE), the Southern Erft block is characterized by a complex tectonic setting that influences hydrological and geological conditions on a local as well as regional level. The study area is located in the south of North Rhine-Westphalia and traversed by several NW-SE-oriented fault structures. Since the tectonic structures were located by past studies based on a sparse foundation of geological data, the positions include considerable uncertainties. Therefore, it was decided to re-evaluate and refine the assumed fault locations by conducting geophysical measurements. Seismic Refraction Tomography (SRT) as well as Electrical Resistivity Tomography (ERT) was performed along seven measurement profiles with a length of up to 1.1 km. In addition to compiling individual resistivity and velocity models for all deduced measurements, ERT and SRT datasets were cooperatively inverted using the Structurally Coupled Cooperative Inversion (SCCI). This algorithm strengthens structural similarities between velocity and resistivity by adapting the individual regularizations after each model iteration. Previously assumed locations of the tectonic structures diverge from the new evidence based on ERT and SRT surveys. Especially in the western and eastern parts of the research area, differences between the survey results and formerly assumed locations are in the order of 100 m. Seismic and geoelectric measurements further indicate a fault structure in the southern part of the area, which remained undetected by past studies. The cooperative inversions do not improve the geophysical models qualitatively, since the individually inverted datasets already provide results of good quality and resolution.
Cite as
Menzel, N. and Klitzsch, N. and Altenbockum, M. and Müller, L. and Wagner, F.M. (2024): Prospection of faults on the Southern Erftscholle (Germany) with individually and jointly inverted refraction seismics and electrical resistivity tomography. Journal of Applied Geophysics. https://doi.org/10.1016/j.jappgeo.2024.105549
Conference contributions
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Dynamic Optimized Experimental Design Strategies for Geoelectrical Monitoring of Subsurface Flow Processes
2025 |
NSG 2025: 31st Meeting of Environmental and Engineering Geophysics, Sep 2025, Volume 2025
Conference websiteAbstract
This study explores ERT survey optimization techniques for monitoring dynamic subsurface processes. Building on well established Optimized Experimental Design (OED) algorithms, we propose a model-driven design that focuses the measurement on those regions of the model space that are affected by the underlying transport process at a specific time step. The approach incorporates a time-dependent focusing mask and accounts for parameter uncertainty by incorporating a variety of hydraulic parameter distributions into the focusing process. We further introduce a hybrid OED strategy that effectively reduces simulation uncertainties by including the already acquired data of past monitoring steps into the evaluation process.
Cite as
Menzel, N. and Uhlemann S. and Wagner, F.M. (2025): Dynamic Optimized Experimental Design Strategies for Geoelectrical Monitoring of Subsurface Flow Processes. NSG 2025: 31st Meeting of Environmental and Engineering Geophysics, Sep 2025, Volume 2025. https://doi.org/10.3997/2214-4609.202520112
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Radioactive Contamination Risk Assessment in Long-Term Radioactive Waste Disposal: Actionable Data-Hub for Analysis-Readiness in Process and Impact Models
2025 |
EGU General Assembly, Vienna, 27 April - 2 May 2025
Conference websiteAbstract
Germany is currently conducting a site selection procedure with the quest for an optimal repository site for high-level radioactive waste in geological subsurface. The site selection procedure must be done in accordance with the Final Repository Safety Requirements Ordinance, which restricts the maximum allowable exposure for high-level radioactive waste released from the final repository site. One of the potential risks associated with the repository site is the release of radionuclides through groundwater flow. Therefore, a risk assessment regarding the environmental impact of different hazard scenarios is crucial to carefully select and ensure long-term safety of the repository site. To assess the risk of radioactive contamination in the subsurface, physics-based process models are implemented to predict the spatial-temporal evolution of the radionuclide concentration associated with a given hazard scenario. The resulting radionuclide concentration provides the basis for impact modelling, namely estimating accumulated dose and subsequently quantifying potential radioactive contamination. Simulations are implemented through the OpenGeoSys software. A supporting Python package, Yaml2Solver, is developed to orchestrate process and impact modelling along with relevant parameters. The package centralizes simulation and material information in YAML files to define and adjust model parameters, and it enables simulating different coupled-level process models. These data-integrated models, however, are built in the presence of uncertainties in material properties, including permeability of rock and groundwater flow. Accounting for uncertainties in physics-based simulations calls for an effective and reliable uncertainty management tool. We therefore developed an analysis-ready and actionable data-hub. The data-hub consists of a database integrated with a graphic user interface (GUI). The database provides material properties along with their uncertainty margins and sensible defaults in YAML files for analysis readiness of simulation models. The material properties are associated with synthetic, reference, and candidate sites, enabling the compilation of site-specific scenarios for simulations. The GUI provides detailed visualization for each site, including a three-dimensional geostructural model, a chronostratigraphic chart indicating the geological formation time of each stratum, and a table providing information on rock properties and attributes of sensible defaults. The data-hub framework supports for systemic and uncertainty-informed model-based assessment as well as subsequent model-based decision-making tasks. We further integrated the data-hub with Yaml2Solver for efficient uncertainty management across various scenarios. Data-hub integerated process and impact modelling offers benefits for managing long-term uncertainties and improving reproducibility, and thereby increasing the transparency and reliability of decision-making. Depending on the material properties with their marginal values sourced from different sites, we construct various site-specific process models. Subsequently, the process models are extended to impact models, describing spatial-temporal evolutions of radiation. The resulting uncertainty-informed impact models enable us to quantify potential radioactive contamination in specific sites and offer valuable insights in repository site selection and safety assessments.
Cite as
Chen, Qian and Boxberg, Marc S. and Menzel, Nino and Wagner, Florian M. and Kowalski, Julia (2025): Radioactive Contamination Risk Assessment in Long-Term Radioactive Waste Disposal: Actionable Data-Hub for Analysis-Readiness in Process and Impact Models. EGU General Assembly, Vienna, 27 April - 2 May 2025. https://doi.org/10.5194/egusphere-egu25-18973
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Surrogate-assisted Bayesian inference with ERT data for contaminant transport modelling in the subsurface
2025 |
EGU General Assembly, Vienna, 27 April - 2 May 2025
Conference websiteAbstract
Understanding and predicting groundwater contaminant transport is inherently challenging due to uncertainties in both field-specific properties and contaminant-related parameters. These uncertainties pose challenges for effective environmental management, including project planning, non-invasive long-term monitoring, and remediation efforts. To address this, we propose a framework that combines geophysical monitoring, surrogate-assisted Bayesian inference, and dimensionality reduction techniques to quantify and reduce these uncertainties and aid in decision making processes. For the implementation of Bayesian inference, our work focuses on electrical resistivity tomography, a geophysical method that is particularly well-suited for the abovementioned purpose due to its sensitivity to variations in fluid content and temperature. The proposed approach addresses two major computational challenges. First, Bayesian inference requires extensive model runs, which can become computationally prohibitive for large domains with fine grids, multiple processes, and multiple time steps. To mitigate this, we use surrogate models that approximate the full physics-based model using input-output data pairs, significantly reducing computational costs. Second, the high-dimensional nature of ERT data complicates both surrogate training and Bayesian inference. High output dimensions lead to increased training times, larger data requirements, and difficulties in likelihood estimation due to the "curse of dimensionality." To overcome this, we incorporate dimension reduction techniques into the framework. Our main focus is to evaluate how surrogate modeling approximations and dimension reduction strategies influence the accuracy and efficiency of Bayesian inference when using ERT measurements for contaminant transport applications. We apply our framework on a 2D synthetic non-reactive contaminant transport scenario, integrating ERT measurements while accounting for uncertainties in both field-specific and contaminant-related parameters. This methodology provides a practical tool for subsurface engineering, offering improvements in planning, parameter estimation, and long-term monitoring to enhance contaminant transport predictions and remediation strategies.
Cite as
Morales Oreamuno, Maria Fernanda and Menzel, Nino and Oladyshkin, Sergey and Wagner, Florian M. and Nowak, Wolfgang (2025): Surrogate-assisted Bayesian inference with ERT data for contaminant transport modelling in the subsurface. EGU General Assembly, Vienna, 27 April - 2 May 2025. https://doi.org/10.5194/egusphere-egu25-12561
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Working towards a software package for Optimized Experimental Design for Electrical Resistivity Tomography
2025 |
85. Jahrestagung der Deutschen Geophysikalischen Gesellschaft, 24.-27. Februar, Bochum
Conference websiteNote: This contribution was awarded with the Best Poster Award at the DGG 2025 conference.Abstract
The noninvasive monitoring of both static structures as well as dynamic transport processes in the subsurface through geophysical methods has gained increasing attention in recent decades. Electrical resistivity tomography (ERT) is particularly well-suited for this purpose due to its sensitivity to variations in fluid content and temperature. Optimizing the measurement layout for ERT is crucial when considering hazardous, static or moving subsurface targets since it ensures accurate characterization of subsurface features by maximizing image resolution and thereby minimizing risks. In particularly in sensitive environments, such as contaminated groundwater zones, radioactive waste repositories, unstable slopes or areas with unexploded ordnances, an optimized ERT array can enhance resolution and sensitivity to improve delineation and risk assessment. Additionally, for moving targets, such as groundwater plumes, tailored configuration schedules enable the detection of dynamic changes over time, supporting effective monitoring strategies. Intensive research in the past decades yielded many different approaches to Optimized Experimental Design (OED) that share the goal of maximizing the information content of a dataset, while keeping the survey expenses to a minimum. Typically performed before field measurements, OED ensures efficient data acquisition and avoids additional steps. However, the process involves labor-intensive stages, from creating a subsurface model to generating hardware-compatible ERT acquisition schemes. This study introduces a comprehensive, modular software package for OED in ERT, streamlining the optimization process and producing acquisition templates with minimal input. The software comprises four key components: 1) Input: Incorporates prior information, including target characteristics, geology, and subsurface flow simulations. 2) Masking: Uses focusing functions to prioritize critical areas, enhancing the cost-benefit ratio. 3) Optimization: Provides stochastic and deterministic algorithms for goal-specific ERT designs. 4) Export: Generates machine-readable schemes compatible with common ERT devices. This modular design ensures flexibility across diverse applications, supports case-specific adaptations, and allows users to integrate custom algorithms under a permissive open-source license. By simplifying the OED process while enabling user-specific innovations, this tool enhances ERT measurement efficiency and adaptability.
Cite as
Menzel, N. and Uhlemann, S. and Wagner, F.M. (2025): Working towards a software package for Optimized Experimental Design for Electrical Resistivity Tomography. 85. Jahrestagung der Deutschen Geophysikalischen Gesellschaft, 24.-27. Februar, Bochum.
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Strategies for geoelectrical monitoring of subsurface fluid transport processes using Optimized Experimental Design
2024 |
EGU General Assembly, Vienna, 14-19 April 2024
Conference websiteAbstract
Electrical resistivity tomography (ERT) offers noninvasive monitoring capabilities for a wide range of environmentally relevant subsurface processes. Its sensitivity to fluid content and temperature changes positions it as an important tool for capturing dynamic processes such as the transport of groundwater pollutants, CO2 or radionuclides. Particularly crucial is its ability to achieve this without intrusively accessing to the site, making it highly valuable in closed repositories like high-level radioactive waste (HLW) storage sites. In highly sensitive and complex environments, as in the case of closed repositories, it is critical to maximize the information content of the planned (geo)physical measurements while keeping the costs to a minimum. Several past studies presented approaches to optimize both the sensor positions and the measurement configurations of ERT surveys for static or moving targets in the subsurface. This study extends Optimal Experimental Design (OED) strategies for geoelectrical measurements using information of active time-dependent transport processes in the subsurface. We present three different approaches for process monitoring and apply them to a simulated diffusive-advective transport process in a synthetic model over several time steps. The methods aim at focusing the survey only on the relevant part of the model, in this case the model region that is affected by the transport process. All presented approaches account for uncertain model input parameters by introducing an uncertainty factor in the ranking function. We present a purely model-driven and a purely data-driven active time-dependent OED approach. The first method utilizes the already acquired data from previous time steps to create predictive focusing masks for the next data set, the latter purely relies on model predictions to focus the survey. Moreover, we delineate a hybrid approach using both the simulated transport distance and the already acquired datasets. All three OED methods are compared to each other as well as to datasets that were acquired using standard electrode configurations. The results of our synthetic study show that the adaptively designed, time-dependent OED approaches result in increased image quality compared to both standard surveys as well as time-independent OED methods. For slow transport processes or small monitoring intervals, the purely data-driven approach is most suitable, since no model predictions, and thus no possible model parametrization uncertainties, are incorporated. For faster transport processes or monitoring strategies with larger acquisition intervals, the strategies that (partly) incorporate model predictions provide the most promising results.
Cite as
Menzel, N. and Uhlemann, S. and Wagner, F. M. (2024): Strategies for geoelectrical monitoring of subsurface fluid transport processes using Optimized Experimental Design. EGU General Assembly, Vienna, 14-19 April 2024.
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The site selection data hub: a data-centric approach for integrated simulation workflow management in radioactive waste disposal site selection
2024 |
EGU General Assembly, Vienna, 14-19 April 2024
Conference websiteAbstract
Given the importance of ensuring the safe disposal of radioactive waste, it is vital to understand the targeted subsurface systems and to build physics-based models to predict their dynamic responses to human interventions. Constructing robust predictive models, however, is very challenging due to the systems' complexity as well as the scarcity and cost of geophysical data acquisition. Optimal matching of data acquisition and predictive simulations is therefore necessary and can be achieved via integrating predictive process modeling, Bayesian parameter estimation, and optimal experimental design into a modular workflow. This allows to quantify the information content of measurement data and therefore enables optimal planning of data acquisition and monitoring strategies. Conducting such data-integrated simulation studies, however, requires a robust workflow management that ensures reproducibility, error management, and transparency. To meet this demand, we established a data-centric approach to workflow control combining error-managed simulations with a functional data hub, providing simulations with direct access to a database of essential material properties. The latter are being made available as site specific scenario compilations along with uncertainty margins and meta information. The data hub serves as an interface facilitating seamless data and simulation exchange to support subsequent model-driven decision-making processes and guarantees that simulations are conducted using manageable, comparable, and reproducible test cases. Furthermore, it ensures that the simulation results can be readily transferred to a designated repository allowing for real-time updates of the model. The implementation of the data hub is based on a Python-based framework for two different use cases: 1) GUI-based use case: The graphical user interface (GUI) facilitates data import, export, and visualization, featuring distinct sections for geographic data representation, structured table organization, and comprehensive visualization of physical properties in varying dimensions. 2) Module-based use case: Built on the YAML-based data-hub framework, it enables direct integration of simulation modules storing measurements and model parameters in the YAML data format. The data is systematically organized to furnish a versatile data selection framework that allows information to be extracted from a variety of references, including specific on-site measurements, laboratory measurements and other references, thereby enabling a comprehensive exploration of different reference-oriented scenarios. This study showcases the data hub as a management infrastructure for executing a modular workflow. Multiple models—such as process and impact models as well as their surrogates and geophysical inverse models—are generated within this workflow utilizing scenarios provided by the data hub. Our study shows that adopting a data-centric approach to control the simulation workflow proves the feasibility of conducting different data-integrated simulations and enhances the interchangeability of information across different stages within the workflow. The paradigm of sustainable model development ensures reproducibility and transparency of our results, while also offering the possibility of synergetic exchange with other research areas.
Cite as
Chen, Q. and Boxberg, M. S. and Menzel, N. and Morales Oreamuno, M. F. and Nowak, W. and Oladyshkin, S. and Wagner, F. M. and and Kowalski, J (2024): The site selection data hub: a data-centric approach for integrated simulation workflow management in radioactive waste disposal site selection. EGU General Assembly, Vienna, 14-19 April 2024.
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Strategies for geoelectrical monitoring of subsurface fluid transport processes using Optimized Experimental Design
2024 |
84. Jahrestagung der Deutschen Geophysikalischen Gesellschaft, 10.-14. März, Jena
Conference websiteAbstract
In highly sensitive and complex environments, such as closed repositories, it is crucial to enhance the information content of planned (geo)physical measurements while keeping the costs to a minimum. Previous studies have proposed methods to optimize both sensor positions and measurement configurations for Electrical Resistivity Tomography (ERT) surveys in subsurface environments with static or moving targets. This study extends Optimal Experimental Design (OED) strategies for geoelectrical measurements by incorporating information from active time-dependent transport processes in the subsurface. Three distinct approaches for process monitoring are presented and applied to a simulated diffusive-advective transport process across multiple time steps. The methods aim at focusing the survey only on the relevant part of the model, in this case the model region that is affected by the transport process. All methods consider uncertain model input parameters by introducing an uncertainty factor in the ranking function. The study introduces a purely model-driven and a purely data-driven time-dependent OED approach. The former relies solely on model predictions to focus the survey, while the latter utilizes previously acquired data to generate predictive focusing masks for the next dataset. Additionally, a hybrid approach combining simulated transport distance and already acquired datasets is outlined. Comparative analyses show that the adaptively designed, time-dependent OED approaches result in increased image quality compared to both standard surveys as well as time-independent OED methods. For slow transport processes or small monitoring intervals, the purely data-driven approach is deemed most suitable, as it does not involve model predictions and, therefore, avoids potential uncertainties in model parametrization. Conversely, for faster transport processes or monitoring strategies with larger intervals, the approaches that (partly) incorporate model predictions show the most promising results.
Cite as
Menzel, N. and Uhlemann, S. and Wagner, F. M. (2024): Strategies for geoelectrical monitoring of subsurface fluid transport processes using Optimized Experimental Design. 84. Jahrestagung der Deutschen Geophysikalischen Gesellschaft, 10.-14. März, Jena.
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Ein Exponat zur Veranschaulichung von seismischen Wellen für die Öffentlichkeitsarbeit
2024 |
84. Jahrestagung der Deutschen Geophysikalischen Gesellschaft, 10.-14. März, Jena
Conference websiteNote: This conference contribution resulted from a hands-on geophysical experiment at the RWTH science night in November 2023.Abstract
Die Vorführung von seismischen Experimenten in Innenräumen für die Öffentlichkeitsarbeit ist oftmals nicht direkt möglich. Idealisierungen oder Miniaturisierungen sind in solchen Fällen erforderlich. Daher haben wir ein Exponat zur Veranschaulichung von seismischen Wellen in Tischgröße konzipiert. Mit unterschiedlich schweren und großen Fallgewichten, die von einem Gestell aus verschiedenen Höhen fallen gelassen werden, können seismische Wellen erzeugt und mit einem RaspberryShake aufgezeichnet werden. Es wurden verschiedene Materialien (Sand, Schaumstoff und Styropor) verwendet, um deren Einfluss auf die Wellenform zu illustrieren. Für die Aufzeichnung und Visualisierung wurde eine Webapplikation entwickelt, welche die Daten des RaspberryShakes kontinuierlich anzeigte. Dazu wurde über einen STA-LTA-Trigger eine Aufzeichnungsmöglichkeit implementiert, so dass verschiedene Seismogramme verglichen werden konnten. Darüber hinaus wurden Gamification-Elemente eingebaut. So konnten Teilnehmer versuchen vorab aufgezeichnete Seismogramme zu reproduzieren. Außerdem konnten, ähnlich wie bei der Jahrmarktattraktion Hau den Lukas, Signale einer bestimmten Stärke erzeugt werden. Hier sollte dann aber nicht eine möglichst starke Amplitude erzeugt werden, sondern eine vorgegebene Amplitude möglichst genau getroffen werden. Ergänzend wurden noch didaktisch aufbereitete Materialien zur Erklärung von aktiver Seismik und der Untergrunderkundung geliefert. Das Exponat wurde bereits erfolgreich auf der RWTH-Wissenschaftsnacht 5 vor 12 im Herbst 2023 eingesetzt und wird stetig weiterentwickelt.
Cite as
Boxberg, M. S. and van Meulebrouck, J. and Balza Morales, A. and Menzel, N. and Wagner, F. M. (2024): Ein Exponat zur Veranschaulichung von seismischen Wellen für die Öffentlichkeitsarbeit. 84. Jahrestagung der Deutschen Geophysikalischen Gesellschaft, 10.-14. März, Jena.
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Prospection of faults in the Southern Erftscholle with Refraction Seismics and Electrical Resistivity Tomography
2023 |
EGU General Assembly, Vienna, 23–28 April 2023
Conference websiteNote: This conference contribution resulted from Nino's master thesis project.Abstract
As part of the Lower Rhein Embayment (LRE), the Southern Erft block is characterized by a complex tectonic setting that may influence hydrological and geological conditions on a local as well as regional level. The area presented in this study is located near Euskirchen in the south of North Rhine-Westphalia and traversed by several NW-SE-oriented fault structures. Past studies based on the lithological description of borehole cores and hydrological measurements stated that the present faults affect the local groundwater conditions throughout the targeted area. However, since the tectonic structures were located based on a sparse foundation of geological borehole data, the results include considerable uncertainties. Therefore, it was decided to re-evaluate and refine the assumed fault locations by conducting geophysical measurements. Seismic Refraction Tomography (SRT) as well as Electrical Resistivity Tomography (ERT) was performed along seven measurement profiles with a length of up to 1.1 km. To allow a sufficient degree of model resolution, the electrode spacing was set to 5 m and halved for areas proximate to assumed fault locations. The geophone spacing was set to 2.5 m for all conducted seismic surveys. A large portion of data processing and inversion was performed with the open-source software package pyGIMLi (Rücker et al., 2017). In addition to compiling individual resistivity and velocity models for all deduced measurements, both ERT and SRT datasets were jointly inverted using the Structurally Coupled Cooperative Inversion (SCCI). This algorithm strengthens structural similarities between velocity and resistivity by adapting the individual regularizations after each model iteration. This study emphasizes the benefit of multi-method geophysics to detect small-scale tectonic features. The surveys allowed to identify the fault locations throughout the area of interest, provided that the vertical displacements are large enough to be detected by the measurements. Previously assumed locations of the tectonic structures diverge from the new evidence based on ERT and SRT surveys. Especially in the western and eastern parts of the research area, differences between the survey results and formerly assumed locations are in the order of 100 m. Seismic and geoelectric measurements further indicate a fault structure in the southern part of the area, which remained undetected by past studies. The joint inversion provides minor improvements of the geophysical models, as most of the individually inverted datasets already provide results of good quality and resolution. Therefore, the effect of the SCCI algorithm is limited to underlining lithological and hydrological boundaries that are already present in the individually inverted ERT- and SRT-models.
Cite as
Menzel, N. and Klitzsch, N. and Altenbockum, M. and Müller, L. and Wagner, F. M. (2023): Prospection of faults in the Southern Erftscholle with Refraction Seismics and Electrical Resistivity Tomography. EGU General Assembly, Vienna, 23–28 April 2023.
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Prospektion von Verwerfungen auf der südlichen Erftscholle mittels ERT und SRT
2023 |
83. Jahrestagung der Deutschen Geophysikalischen Gesellschaft, 5.-9. März, Bremen
Conference websiteNote: This conference contribution resulted from Nino's master thesis project and received the best poster award.Abstract
Komplexe tektonische Verhältnisse der südlichen Erftscholle beeinträchtigen insbesondere auf kleinräumigen Skalen die natürlichen geologischen und hydrologischen Verhältnisse. Das präsentierte Gebiet nahe Euskirchen wird von mehreren NW-SE-gerichteten Verwerfungen durchzogen, deren Lage sowie Einfluss auf die vorherrschenden Bedingungen bereits in vergangenen Studien ermittelt wurde. Da sich diese Untersuchungen jedoch ausschließlich auf räumlich punktuelle Datenquellen stützen, enthalten die Ergebnisse grosse Unsicherheiten. Die in dieser Studie beschriebenen geophysikalischen Messungen sollen dabei helfen, die angenommenen Störungsverläufe im Arbeitsgebiet zu evaluieren und gegebenenfalls zu korrigieren. Seismische Refraktionstomografie (SRT) und elektrische Widerstandstomografie (ERT) wurden entlang von Messprofilen möglichst orthogonal zu den vermuteten Störungslagen durchgeführt. Ein Grossteil der Datenverarbeitung sowie die Inversionen wurden mittels der frei verfügbaren Software pyGIMLi (Rücker et al., 2017) durchgeführt. Zusätzlich zu den individuellen Inversionen der SRT- und ERT-Datensätze wurde der Structurally-Coupled Cooperative Inversion (SCCI) Algorithmus (Skibbe et al., 2018) verwendet, um die seismischen und geoelektrischen Daten gemeinsam zu invertieren. Diese Studie zeigt die Vorteile der individuellen und kombinierten Anwendung mehrerer geophysikalischer Methoden im Kontext oberflächennaher Untersuchungen, insbesondere hinsichtlich der Detektion kleinräumiger tektonischer Strukturen. Die Lage der Verwerfungen konnte im gesamten Arbeitsgebiet mittels geophysikalischer Tomografien identifiziert werden, sofern der vertikale Versatz an den Störungen gross genug ist, um von den Methoden dargestellt zu werden. Aufgrund der guten Auflösung der Einzelinversionen greift der SCCI-Algorithmus lediglich an den bereits erkennbaren lithologischen und hydrologischen Modellgrenzen und stellt diese verdeutlicht dar. Durch wiederholte Anpassung der Regularisierung nach jeder Iteration ermöglicht diese Methode den Austausch struktureller Informationen zwischen den individuellen geophysikalischen Datensätzen während der Inversion.
Cite as
Menzel, N. and Klitzsch, N. and Altenbockum, M. and Müller, L. and Wagner, F. M. (2023): Prospektion von Verwerfungen auf der südlichen Erftscholle mittels ERT und SRT. 83. Jahrestagung der Deutschen Geophysikalischen Gesellschaft, 5.-9. März, Bremen.